Methods and systems for providing images for facilitating communication

ABSTRACT

Aspects of the disclosure include computer-implemented methods and systems for providing generative adversarial network (GAN) digital image data. GAN digital image data corresponding to a suggested transaction for an identified customer can be determined.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.16/425,347, filed May 29, 2019, the content of which is incorporatedherein by reference in its entirety.

This application is related to U.S. patent application Ser. No.16/425,248, which is titled “Methods And Systems For Providing ChangesTo A Voice Interacting With A User”, filed May 29, 2019, Attorney Docketno. 359025-100174, the content of which is incorporated herein byreference in its entirety.

FIELD

The present disclosure relates generally to providing images forfacilitating communication.

BACKGROUND

When people are visiting an establishment (e.g., a store or restaurant),they often wish to communicate (e.g., in order to make a transaction).For example, when a person cannot understand either spoken or writtencommunications related to a transaction, help may be needed in order forthe person to complete the transaction. There is a need to provide helpto make the process easier.

SUMMARY

Aspects of the disclosure include a computer-implemented method forinteracting with a user. Identity information for a user can bereceived. The identity information can be analyzed to identify the user.User information for an identified user can be retrieved, the userinformation indicating that a voice interacting with the identified useris to be translated into image data to help the identified usercommunicate with the voice. Translated image data translating voiceinstructions by the voice can be retrieved. The translated image datacan be displayed to the identified user.

Aspects of the disclosure can also include accepting instructions fromthe identified user based on user interaction with the translated imagedata. The translated image data can include photographic image dataand/or non-photographic image data. The photographic image data can begenerated using a generative adversarial network (GAN). A sequence ofvoice instructions can be translated into a sequence of photographicimages using the GAN. The translated image data can include pre-definedimages representing possible transactions.

Aspects of the disclosure can include retrieving historical transactioninformation for the user, and determining a suggested transaction forthe user based on the historical transaction information. Aspects of thedisclosure can also include: determining the suggested transaction usingprevious transactions made by other users with demographics similar tothe user; determining the suggested transaction using previoustransactions made by the user; or determining the suggested transactionusing a current location of the user and previous transactions made atthe current location; or any combination thereof

Aspects of the disclosure can include a system for interacting with auser, the system including: a memory storing instructions and aprocessor. The processor can be configured to: receive physiologicalinformation and/or behavioral information for the user representingidentifying information for a user; analyze the physiologicalinformation and/or the behavioral information for the user to determinean identity of the user; determine a need to provide photographic imagedata translating voice communication from a voice interacting with theuser; display translated photographic image data translating the voiceinteracting with the user using a generative adversarial network (GAN);and accept instructions from the user.

According to some aspects, the translated photographic image data can bedisplayed in a video format. The processor can also be configured to:play audio data while the translated photographic image data isdisplayed; and/or display text data while the translated photographicimage data is displayed.

According to some aspects, the processor can also be configured to:receive feedback information comprising body language information forthe user; determine if the body language information signifies: aninability to understand the voice, a misunderstanding of the voice, ordispleasure with a user experience, or any combination thereof; analyzethe body language information in order to identify help information; andstore the help information for the user.

According to some aspects, the processor can be configured to: receivefeedback information comprising language spoken by the user; determineif the language spoken by the user includes pre-defined wordssignifying: an inability to understand the voice, a misunderstanding ofthe voice; or displeasure with a user experience, or any combinationthereof; analyze the language spoken by the user in order to identifyhelp information; and store the help information for the user.

According to some aspects, the processor can be configured to: receivefeedback information from the user, the feedback information indicatingdifficulty the user has with understanding the transaction option;analyze the feedback information in order to identify help information;and store the help information for the user. The feedback informationcan include survey information.

According to some aspects, the processor can be configured to: receivefeedback information from the user related to the appearance of a GANtranslated photographic image; and store the feedback information forfuture application. The feedback information can include preferencesrelated to: color, template, outline, font, or font size, or anycombination thereof.

According to some aspects, a device can be provided for communicatingwith a user. The device can include a memory storing instructions and aprocessor. The processor can be configured to: display translatedphotographic image data translating a voice interacting with the userusing a generative adversarial network (GAN); and accept instructionsfrom the user. The processor can also be configured to determine a needto provide the translated photographic image data. The processor canalso be configured to: determine multiple transaction options for anidentified user based on historical data; and display an image for eachtransaction option. The processor can also be configured to: determine aprobability for each transaction option; and display transaction optionimages in order of probability.

BRIEF DESCRIPTION OF THE DRAWINGS

Various objectives, features, and advantages of the disclosed subjectmatter can be more fully appreciated with reference to the followingdetailed description of the disclosed subject matter when considered inconnection with the following drawings, in which like reference numeralsidentify like elements.

FIG. 1 is a diagram of an illustrative system 100 for interacting with auser, according to some embodiments of the present disclosure.

FIG. 2 is another diagram of an illustrative system 200 for interactingwith a user, according to some embodiments of the present disclosure.

FIG. 3 is a flow diagram 300 showing processing that may occur whenproviding an insight within the system of FIGS. 1 and/or 2, according tosome embodiments of the present disclosure.

FIG. 4 is a flow diagram 400 showing processing that may occur whenidentifying a user within the system of FIGS. 1 and/or 2, according tosome embodiments of the present disclosure.

FIG. 5 illustrates a flow diagram 500 showing processing that may occurwhen analyzing and storing feedback information within the system ofFIGS. 1 and/or 2, according to some embodiments of the presentdisclosure.

FIG. 6 illustrates an example computer 600, according to someembodiments of the present disclosure.

The drawings are not necessarily to scale, or inclusive of all elementsof a system, emphasis instead generally being placed upon illustratingthe concepts, structures, and techniques sought to be protected herein.

DETAILED DESCRIPTION OF EMBODIMENTS OF THE INVENTION

Embodiments of the present disclosure may help facilitate communicationwith a user (e.g., in order to help complete a transaction). Forexample, when people are going to an establishment (e.g., a bank,restaurant/bar, movie theater, theme park, sports venue, music venue,etc.) or visiting an establishment's physical mobile site, web site ormobile device application, it may make it easier for them to communicateand/or complete a transaction if they can better communicate and/or if arelevant transaction were suggested. Examples using a bankinginstitution establishment are discussed below. However, those ofordinary skill in the art will see that the system may also be used bymany other types of establishments.

FIG. 1 is a diagram of an illustrative system 100 for communicating witha user in order to complete a transaction, according to some embodimentsof the present disclosure. As shown in FIG. 1, a client 160 can access(e.g., over a network 150) an identification module 110, a convert voiceto image module 115, a determine historical data module 120, a displaymodule 130, and a feedback module 140.

The identification module 110 can identify the user using analyzedidentity information. In some aspects of the disclosure, a credit card,photo ID, or other type of identification can be analyzed to identifythe user. In other aspects, physiological information and/or behavioralinformation can be received from the user and provided to anidentification system, and used to identify the user. The physiologicalinformation can include: iris data; retina data; eye vein data;fingerprint data; hand geometry data; facial data; or finger vein data;or any combination thereof. The behavioral information can includesignature information and/or voice information.

For example, the user can interact with a system that scans her eye,checks her fingerprint, hand, face or finger, or any combinationthereof. In addition, the user can be asked to provide a signature ortalk so that her signature or voice can be identified by the system.

The determine historical data module 120 can: review historicalinformation for the identified user, provide needed help information, orsuggest one or more transactions based on this analyzed historicaltransaction data, or any combination thereof. The historical informationcan include feedback information and/or needed help information (e.g.,such as whether or not the user would benefit from use of the convertvoice to image module 115).

For example, the historical information can determine if previousfeedback information has been provided for an identified user. Forexample, it can be determined that the identified user has had troubleunderstanding the voice of a customer service representative and/or arecorded voice interacting with the user. In this case, the convertvoice to image module 115 can be used to help the user understand whatthe customer service representative and/or the recorded voice aresaying. Multiple examples of other types of feedback information aredescribed below in more detail.

The historical information can include previous transactions made by theuser and/or other users with demographics similar to the user. Thehistorical transaction information may also include information aboutwhere the user makes different types of transactions (e.g., at anestablishment's physical site, a physical mobile site, a web site, amobile device application, etc.).

If the system is a banking system, possible transactions made by theuser can include: opening an account, deposit funds (e.g., cash, check,electronic funds), transfer funds (e.g., within or out of the bankingsystem), withdraw funds (e.g., cash, change in specific denominations($1 bills, $5 bills, quarters, etc.), view information on an account(e.g., checking, savings, line of credit, mortgage, car loan, otherloan), pay a bill (e.g., make a one time payment or set up an autopayment), obtain a cashier's check and/or money order, apply for amortgage or other loan, get an item notarized, request information foran account issue, etc.

For example, historical transaction information for a particular usercan indicate that this user usually only uses the banking institution'sphysical banking machine to withdraw the maximum amount of cash allowed.Thus, once the banking system identifies the user as using one of theirphysical banking machines, the banking system can suggest that the userwishes to withdraw the maximum amount of cash allowed.

As another example, historical transaction information for a particularuser can indicate that this user transfers $500 (using the bankinginstitution's mobile application) into her 20 year old daughter'saccount on the first of every month. Thus, if the user logs onto themobile application at the first of the month, it can suggest that theuser transfer $500 to her daughter's account.

As a further example, historical transaction information for aparticular user can indicate that this user visit's the establishment'sweb site at the end of the month to pay a car loan. Thus, if the userlogs in to the establishment's web site at the end of the month, it cansuggest that the user pay the car loan payment.

For example, historical transaction information for users in a person'sdemographic (e.g., men over 60) can indicate that it is common for userswithin this demographic withdraw cash every other Friday (e.g., payday).As another example, historical information for users in another person'sdemographic (e.g., men between 18 and 30) can indicate that it is commonfor these users to electronically transfer funds (e.g., move money fromchecking to savings account) and pay bills (e.g., credit card bill,telephone bill) every other Friday (e.g., payday).

The convert voice to image module 115 can translate the voicecommunicating with the user into one or more images. The translatedimages can be photo images and/or non-photo images. For example, forelderly people with hearing issues, they may also not be able to read(e.g., in a particular language or in any language). In this case,displaying text may not help and images may be more useful for the user.

In some embodiments, the convert voice to image module 115 can generatephotos using a generative adversarial network (GAN). The GAN can be adeep learning model that can translate one or more voice instructions toone or more photo instructions that are easy for the user to understand.In some embodiments, the voice instructions can be translated into text.Then, the GAN can synthesize images from the text. More information onthe GAN can be found in the following, which are all herein incorporatedby reference:

-   -   Reed, Scott et al., “Generative Adversarial Text to Image        Synthesys,” Proceedings of the 33rd International Conference on        Machine Learning (New York, N.Y., USA, 2016).    -   Goodfellow, Ian et al., “Generative Adversarial Nets,”        Proceedings of Advances in Neural Information Processing Systems        (NIPS2014) pp. 2672-2680.    -   Apr. 23, 2019 Generative Adversarial Network Wikipedia page        (https://en.wikipedia.org/wiki/Generative adversarial network).

For example, the system can translate the voice saying “click on thegreen button to approve” into text. The GAN can then translate the textinto a photo showing a hand pointing to a green button that has “OK” onit. As another example, the system can translate “show me your debitcard or ID” into a photo showing a debit card and driver's license.

In some embodiments, the convert voice to image module 115 can generatephoto images and/or non-photo images by recognizing key words in thevoice that cause certain pre-determined photo images and/or non-photoimages to be displayed. For example, if the convert voice to imagemodule 115 recognizes the key words “show ID”, a screen can be displayedshowing a photo image or a non-photo image of a debit card, a creditcard, and a driver's license.

The display module 130 can display one or more images representing oneor more suggested transactions. In some embodiments, the images can beactual photographs or appear to be photographs (e.g., an image thatappears to be a photo of a hand pointing to a green button, the photogenerated using a computer). In some embodiments, the images can benon-photographic images (e.g., a computer icon or computer image of ahand pointing to a green button).

Instructions from the user can be accepted based on user interactionwith the image. The images can include pictures visualizing thedifferent types of transactions. The display module 130 can also displayone or more images (e.g., photo images and/or non-photo images) showingactions the user can take with respect to a suggested transaction (e.g.,hand pointing to green button). The display module 130 can also displayone or more images showing actions taken by the establishment (e.g., apicture of a receipt when a receipt is being printed). The images caninclude still images and/or video.

If the system is a banking system, some example transaction images caninclude: deposit funds (e.g., a picture of putting cash into a piggybank), transfer funds (e.g., an image of moving cash from one picture ofa bank to another picture of a bank)), withdraw funds (e.g., an image oftaking cash out of a piggy bank), view information on an account (e.g.,an image of a piece of paper with account information on it)), pay abill (e.g., an image of sending an envelope with money in it), obtain acashier's check and/or money order (e.g., an image of a cashier's checkor money order), apply for a mortgage or other loan (e.g., an image of ahouse, car, etc.), get an item notarized (e.g., an image of a notarystamp), request information for an account issue (e.g., a question markover small image of sample account summary), ask a question (e.g., aquestion mark), etc.

In some aspects of the disclosure, the images can be video images. Forexample, one or more suggested transaction images can be shown as videosthat play once, or over and over until the user chooses a transaction.As another example, the videos can be shown when a user chooses aparticular suggested transaction image.

The system can also play audio data while the image data is displayed.For example, data explaining a particular suggested transaction can beplayed while the image for that suggested transaction is shown.

If multiple transactions are suggested, the transaction can be displayedin order of probability (e.g., with the most probably transaction listedfirst), and any audio data can be played in the same order thetransactions are listed on the screen.

In addition, the system can display text data while the image data isdisplayed. The text data can be displayed near or on the image.

The feedback module can receive feedback information from or for theuser. The feedback information can include: body language informationfor the user, language spoken by the user, or survey information, or anycombination thereof. The feedback module can determine if the bodylanguage information and/or the language spoken by the user signifies:an inability to understand the voice, a misunderstanding of the voice,or displeasure with a user experience, or any combination thereof. Thefeedback module can analyze the body language information in order toidentify help information.

The feedback module can determine any difficulty the user has withunderstanding a transaction option. The feedback module can also analyzethe feedback information in order to identify help information.

The feedback module can also help determine any preferences the user hasfor GAN translated photographic image data. For example, the feedbackmodule can receive feedback information related to the appearance of GANtranslated photographic image data. The feedback information caninclude: body language information for the user, language spoken by theuser, or survey information, or any combination thereof. This feedbackinformation can be stored and applied to generate GAN translatedphotographic image data according to the user's preferences when theuser returns. The feedback information can include preferences relatedto: color, template, outline, font, or font size, or any combinationthereof. Using user feedback on a photo showed to a user, thepreferences of that user can be determined. For example, it can bedetermined that general users (or users in a certain demographic, or aparticular user) tend to like photos better that have: dark colorsrather than light colors, no green and/or blue colors, content focusedat the center (as opposed to spread-out content).

In some embodiments, training data can be restricted to generatesynthetic images based on the user preferences. For example, using theexample feedback provided above, the system can only select syntheticimages with dark colors, simple content focused at the center of thescreen, a big font size, etc. This information can train the GAN model.Thus, when the user returns to the bank, the system can apply the GANmodel.

FIG. 2 is another diagram of an illustrative system 200 for providing asuggested transaction, according to some embodiments of the presentdisclosure. As shown in FIG. 2, a client 260 can access (e.g., over anetwork 150 using server(s) 110) data representing transactions betweencustomers and establishments stored in a database 120. The database 120can store instructions in one or more memories.

FIG. 3 is a flow diagram 300 showing processing that may occur withinthe system of FIG. 1 and/or FIG. 2, according to some embodiments of thepresent disclosure. In step 301, the process can start. In step 315, itcan be determined if a user can be identified. If not, in step 320, theprocess can end. If yes, in step 340, user information can be found. Instep 345, it can be determined if historical data for the identifieduser can be found. If not, the process can move to 362. If yes, theprocess can move to step 355, where suggested transaction options and/orhelp information can be determined. In 360, suggested transactionoptions can be displayed using image (e.g., photo image and/or non-photoimage) data. In 362, a voice (e.g., live or recorded) interacting withthe user can be translated to image (e.g., photo image and/or non-photoimage) data. In 365, instructions from the user can be obtained. In 370,feedback from the user can be obtained. In 375, the process can end.

FIG. 4 is a flow diagram showing additional processing that may occur instep 315 of flow diagram 300 of FIG. 3, according to some embodiments ofthe present disclosure. In 410, physiological information can beobtained from the user. In 420, behavioral information can be obtainedfrom the user. In 430, the user can be asked to self-identify. In 440,the user can be identified using the physiological, behavioral orself-identified information.

FIG. 5 is a flow diagram showing additional processing that may occur instep 365 of flow diagram 300 of FIG. 3, according to some embodiments ofthe present disclosure. In step 510, survey information can be obtainedfrom the user. In step 520, body language can be obtained from the user.In step 530, language can be obtained from the user. In 540, the surveyinformation, the body language information, and/or the languageinformation can be analyzed. In 550, the analyzed survey information,the body language information, and/or the language information can beanalyzed as feedback information.

In some aspects of the disclosure the transaction data can include manydifferent properties related to a transaction. This may include: acustomer name, a customer ID (e.g., anonymous or not) that allowsidentification of a person who is making a purchase and what otherpurchases they have made before and after that transaction, merchant,merchant ID (e.g., anonymous or not), merchant name, location ofmerchant, amount of the purchase, or how a purchase was made (e.g., inperson, online, with APPLE PAY, with card dip, with card swipe, etc.),or any combination thereof. The confluence of seeing a multitude oftransaction data may provide for powerful insights.

Methods described herein may represent processing that occurs within asystem for providing an insight about an establishment (e.g., system 100of FIG. 1 and/or system 200 of FIG. 2). The subject matter describedherein can be implemented in digital electronic circuitry, or incomputer software, firmware, or hardware, including the structural meansdisclosed in this specification and structural equivalents thereof, orin combinations of them. The subject matter described herein can beimplemented as one or more computer program products, such as one ormore computer programs tangibly embodied in an information carrier(e.g., in a machine readable storage device), or embodied in apropagated signal, for execution by, or to control the operation of,data processing apparatus (e.g., a programmable processor, a computer,or multiple computers). A computer program (also known as a program,software, software application, or code) can be written in any form ofprogramming language, including compiled or interpreted languages, andit can be deployed in any form, including as a stand-alone program or asa module, component, subroutine, or other unit suitable for use in acomputing environment. A computer program does not necessarilycorrespond to a file. A program can be stored in a portion of a filethat holds other programs or data, in a single file dedicated to theprogram in question, or in multiple coordinated files (e.g., files thatstore one or more modules, sub programs, or portions of code). Acomputer program can be deployed to be executed on one computer or onmultiple computers at one site or distributed across multiple sites andinterconnected by a communication network.

The processes and logic flows described in this specification, includingthe method steps of the subject matter described herein, can beperformed by one or more programmable processors (e.g., processor 600 inFIG. 6) executing one or more computer programs to perform functions ofthe subject matter described herein by operating on input data andgenerating output. The processes and logic flows can also be performedby, and apparatus of the subject matter described herein can beimplemented as, special purpose logic circuitry, e.g., an FPGA (fieldprogrammable gate array) or an ASIC (application specific integratedcircuit).

FIG. 6 illustrates an example computer 605, according to someembodiments of the present disclosure. Computer 605 can include aprocessor 610 suitable for the execution of a computer program, and caninclude, by way of example, both general and special purposemicroprocessors, and any one or more processor of any kind of digitalcomputer. A processor can receive instructions and data from a mainmemory 630 (e.g., a read only memory or a random access memory or both).Processor 610 can execute instructions and the memory 630 can storeinstructions and data. A computer can include, or be operatively coupledto receive data from or transfer data to, or both, a storage medium 640for storing data (e.g., magnetic, magneto optical disks, or opticaldisks). Information carriers suitable for embodying computer programinstructions and data can include all forms of nonvolatile memory,including by way of example semiconductor memory devices, such as EPROM,EEPROM, flash memory device, or magnetic disks. The processor 610 andthe memory 630 can be supplemented by, or incorporated in, specialpurpose logic circuitry. The computer 605 can also include aninput/output 620, a display 650, and a communications interface 660.

It is to be understood that the disclosed subject matter is not limitedin its application to the details of construction and to thearrangements of the components set forth in the following description orillustrated in the drawings. The disclosed subject matter is capable ofother embodiments and of being practiced and carried out in variousways. Accordingly, other implementations are within the scope of thefollowing claims. Also, it is to be understood that the phraseology andterminology employed herein are for the purpose of description andshould not be regarded as limiting. As such, those skilled in the artwill appreciate that the conception, upon which this disclosure isbased, may readily be utilized as a basis for the designing of otherstructures, methods, and systems for carrying out the several purposesof the disclosed subject matter. It is important, therefore, that theclaims be regarded as including such equivalent constructions insofar asthey do not depart from the spirit and scope of the disclosed subjectmatter.

Although the disclosed subject matter has been described and illustratedin the foregoing exemplary embodiments, it is understood that thepresent disclosure has been made only by way of example, and thatnumerous changes in the details of implementation of the disclosedsubject matter may be made without departing from the spirit and scopeof the disclosed subject matter.

In addition, it should be understood that any figures which highlightthe functionality and advantages are presented for example purposesonly. The disclosed methodology and system are each sufficientlyflexible and configurable such that they may be utilized in ways otherthan that shown. For example, other steps may be provided, or steps maybe eliminated, from the described flows, and other components may beadded to, or removed from, the described systems. In addition, the orderof the steps illustrated or described may be changed.

Although the term “at least one” may often be used in the specification,claims and drawings, the terms “a”, “an”, “the”, “said”, etc. alsosignify “at least one” or “the at least one” in the specification,claims and drawings.

Finally, it is the applicant's intent that only claims that include theexpress language “means for” or “step for” be interpreted under 35U.S.C. 112(f). Claims that do not expressly include the phrase “meansfor” or “step for” are not to be interpreted under 35 U.S.C. 112(f).

1. A computer-implemented method for providing generative adversarialnetwork (GAN) digital image data to a customer, comprising: retrieving,by the computer system, historical transaction information for anidentified customer, the historical transaction information comprisingprevious banking-related transactions completed by the customer andwhich banking option the customer used to make each previousbanking-related transaction; determining, by the computer system, GANdigital image data corresponding to a suggested transaction for theidentified customer, the suggested transaction determined using thepre-designated words in the text and the historical transaction data,the GAN digital image data determined using the GAN user preferences;and displaying, by the computer system, the GAN digital image data tothe identified customer.
 2. The method of claim 1, comprising:receiving, from the computer system, a live voice communication of theservice representative interacting with the identified customer.
 3. Themethod of claim 1, comprising: indicating GAN user preferences for theidentified customer related to the GAN digital image data preferred bythe identified customer.
 4. The method of claim 1, comprising: receivinginstructions, from the computer system, indicating that a live voice ofa service representative interacting with the identified customer is tobe translated into the GAN digital image data to help the identifiedcustomer communicate with the live voice.
 5. The method of claim 1,wherein a sequence of voice instructions is translated into a sequenceof GAN photographic digital images.
 6. The method of claim 1, whereinthe translated GAN digital image data comprises pre-defined GAN digitalimages representing possible transactions.
 7. The method of claim 1,further comprising: accepting, by the computer system, instructions fromthe identified customer based on user interaction with the translatedGAN digital image data.
 8. The method of claim 1, comprising:determining a suggested transaction based on the historical transactioninformation using: previous transactions made by other users withdemographics similar to the identified customer; the previoustransactions made by the identified customer; and a current location ofthe identified customer and the previous transactions made at thecurrent location.
 9. A system for interacting with a customer,comprising; a memory storing instructions; and a processor that, whenexecuting the instructions, is configured to: retrieve historicaltransaction information for the identified customer, the historicaltransaction information comprising previous banking-related transactionscompleted by the customer and which banking option the customer used tomake each previous banking-related transaction; determine GANphotographic digital image data corresponding to a suggested transactionfor the identified customer. the suggested transaction determined usingthe pre-designated words in the text and the historical transactiondata, the GAN digital image data determined using the GAN userpreferences; and display translated GAN photographic digital image datatranslating the live voice interacting with the identified customer. 10.The system of claim 9, wherein the translated GAN photographic digitalimage data is displayed in video form.
 11. The system of claim 9,wherein the processor is configured to: play audio data while thetranslated GAN photographic digital image data is displayed.
 12. Thesystem of claim 9, wherein the processor is configured to: display textdata while translated the GAN photographic digital image data isdisplayed.
 13. The system of claim 9, wherein the processor isconfigured to: receive feedback information comprising body languageinformation for the identified customer; determine if the body languageinformation signifies: an inability to understand the live voice, amisunderstanding of the live voice, or displeasure with a userexperience, or any combination thereof; analyze the body languageinformation in order to identify help information; and store the helpinformation for the identified customer.
 14. The system of claim 9,wherein the processor is configured to: receive feedback informationcomprising language spoken by the customer; determine if the languagespoken by the user comprises pre-defined words signifying: an inabilityto understand the live voice, a misunderstanding of the live voice; ordispleasure with a user experience, or any combination thereof; analyzethe language spoken by the identified customer in order to identify helpinformation; and store the help information for the identified customer.15. The system of claim 9, wherein the processor is configured to:receive feedback information from the identified customer, the feedbackinformation indicating difficulty the identified customer has withunderstanding the transaction option; analyze the feedback informationin order to identify help information; and store the help informationfor the identified customer.
 16. The system of claim 15, wherein thefeedback information comprises survey information.
 17. A device forcommunicating with a customer, comprising: a memory storinginstructions; and a processor that, when executing the instructions, isconfigured to: determine generative adversarial network (GAN) userpreferences related to GAN digital image data preferred by an identifiedcustomer; receive a live voice communication of a service representativeinteracting with the identified customer; translate the live voicecommunication into text; recognize pre-designated words in the text;determine GAN photographic digital image data corresponding to asuggested transaction for the identified customer, the suggestedtransaction determined using the pre-designated words in the text andthe transaction data, the GAN digital image data determined using theGAN user preferences; and display translated GAN photographic digitalimage data translating a live voice interacting with the identifiedcustomer.
 18. The device of claim 17, wherein the processor isconfigured to: determine a need to provide translated GAN photographicdigital image data.
 19. The device of claim 17, wherein banking optionscomprise: at an establishment's physical site, a physical mobile site, aweb site, a mobile device application.
 20. The device of claim 19,wherein the processor is configured to: determine a probability for eachsuggested transaction option; and display transaction option GANphotographic digital images using probability information.